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The Vitals Risk Index - Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System
Journal article   Open access   Peer reviewed

The Vitals Risk Index - Retrospective Performance Analysis of an Automated and Objective Pediatric Early Warning System

Tyler J. Gorham, Steve Rust, Laura Rust, Stacy Kuehn, Jing Yang, James Shuhan Lin, Jeffrey Hoffman, Yungui Huang, Simon Lin, Richard McClead, …
Pediatric quality & safety, Vol.5(2), pp.E271-e271
03/01/2020
DOI: 10.1097/pq9.0000000000000271
PMCID: PMC7190256
PMID: 32426637
url
https://doi.org/10.1097/pq9.0000000000000271View
Published (Version of record) Open Access

Abstract

Introduction: Pediatric in-hospital cardiac arrests and emergent transfers to the pediatric intensive care unit (ICU) represent a serious patient safety concern with associated increased morbidity and mortality. Some institutions have turned to the electronic health record and predictive analytics in search of earlier and more accurate detection of patients at risk for decompensation. Methods: Objective electronic health record data from 2011 to 2017 was utilized to develop an automated early warning system score aimed at identifying hospitalized children at risk of clinical deterioration. Five vital sign measurements and supplemental oxygen requirement data were used to build the Vitals Risk Index (VRI) model, using multivariate logistic regression. We compared the VRI to the hospital’s existing early warning system, an adaptation of Monaghan’s Pediatric Early Warning Score system (PEWS). The patient population included hospitalized children 18 years of age and younger while being cared for outside of the ICU. This dataset included 158 case hospitalizations (102 emergent transfers to the ICU and 56 “code blue” events) and 135,597 control hospitalizations. Results: When identifying deteriorating patients 2 hours before an event, there was no significant difference between Pediatric Early Warning Score and VRI’s areas under the receiver operating characteristic curve at false-positive rates ≤ 10% (pAUC10 of 0.065 and 0.064, respectively; P = 0.74), a threshold chosen to compare the 2 approaches under clinically tolerable false-positive rates. Conclusions: The VRI represents an objective, simple, and automated predictive analytics tool for identifying hospitalized pediatric patients at risk of deteriorating outside of the ICU setting.

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